Python notebook using data from multiple data sources · 820 views · 4mo ago·pandas, e-commerce services

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Input
Data Sources
Online Retail II UCI
online_retail_II.csv
UCI Online Retail II Data Set
online_retail_II.xlsx
Online Retail II UCI
Last Updated: 2 years ago
Context
This Online Retail II data set contains all the transactions occurring for a UK-based and registered, non-store online retail between 01/12/2009 and 09/12/2011.The company mainly sells unique all-occasion gift-ware. Many customers of the company are wholesalers.
Content
Attribute Information:
InvoiceNo: Invoice number. Nominal. A 6-digit integral number uniquely assigned to each transaction. If this code starts with the letter 'c', it indicates a cancellation.
StockCode: Product (item) code. Nominal. A 5-digit integral number uniquely assigned to each distinct product.
Description: Product (item) name. Nominal.
Quantity: The quantities of each product (item) per transaction. Numeric.
InvoiceDate: Invice date and time. Numeric. The day and time when a transaction was generated.
UnitPrice: Unit price. Numeric. Product price per unit in sterling (£).
CustomerID: Customer number. Nominal. A 5-digit integral number uniquely assigned to each customer.
Country: Country name. Nominal. The name of the country where a customer resides.
Acknowledgements
Chen, D. Sain, S.L., and Guo, K. (2012), Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining, Journal of Database Marketing and Customer Strategy Management, Vol. 19, No. 3, pp. 197-208. doi: [Web Link].
Chen, D., Guo, K. and Ubakanma, G. (2015), Predicting customer profitability over time based on RFM time series, International Journal of Business Forecasting and Marketing Intelligence, Vol. 2, No. 1, pp.1-18. doi: [Web Link].
Chen, D., Guo, K., and Li, Bo (2019), Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study, 24th Iberoamerican Congress on Pattern Recognition (CIARP 2019), Havana, Cuba, 28-31 Oct, 2019.
Laha Ale, Ning Zhang, Huici Wu, Dajiang Chen, and Tao Han, Online Proactive Caching in Mobile Edge Computing Using Bidirectional Deep Recurrent Neural Network, IEEE Internet of Things Journal, Vol. 6, Issue 3, pp. 5520-5530, 2019.
Rina Singh, Jeffrey A. Graves, Douglas A. Talbert, William Eberle, Prefix and Suffix Sequential Pattern Mining, Industrial Conference on Data Mining 2018: Advances in Data Mining. Applications and Theoretical Aspects, pp. 309-324. 2018.

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Irem KARAKAYA • Posted on Version 8 of 8 • 4 months ago • Options •
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Good Work, Congrats and thank you !!
Onur AkcakayaTopic Author • Posted on Version 8 of 8 • 4 months ago • Options •
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Thank you for your comment @remkarakaya
Ozan GÜNER • Posted on Version 6 of 8 • 4 months ago • Options •
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Great work👍 Thanks for sharing..
Onur AkcakayaTopic Author • Posted on Version 8 of 8 • 4 months ago • Options •
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Thanks for reviewing it @oktayozangner
Enes Topaçoğlu • Posted on Version 4 of 8 • 4 months ago • Options •
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Great work. Thanks for your sharing 👍
Onur AkcakayaTopic Author • Posted on Version 6 of 8 • 4 months ago • Options •
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Thank you for your review @enestopacoglu
Pinar Dogan • Posted on Version 8 of 8 • 4 months ago • Options •
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Very good notebook about an important topic, RFM. Thank you for sharing!
Onur AkcakayaTopic Author • Posted on Version 8 of 8 • 4 months ago • Options •
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Thank you @pinardogan. I am glad you you like it!!
Mehmet A. • Posted on Version 8 of 8 • 4 months ago • Options •
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what a nice and good kernel congrats @onurakcakaya 🖖
Onur AkcakayaTopic Author • Posted on Version 8 of 8 • 4 months ago • Options •
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@mathchi It is good to know that you like it !!!!
Ritesh Yadav • Posted on Version 6 of 8 • 4 months ago • Options •
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Great work, Very Helpful +upvoted!! :) Please Checkout this notebook feedbacks are very much appreciated!! :)
https://www.kaggle.com/ritesh2000/bert-all-in-one
Nagesh Singh Chauhan • Posted on Version 4 of 8 • 4 months ago • Options •
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Informative Notebook, nicely explained. Thank you for sharing with us. Upvoted.
I have created a notebook on "hugging face transformer basic usage"
Here, https://www.kaggle.com/nageshsingh/huggingface-transformer-basic-usage
Umerkk12 • Posted on Version 8 of 8 • 4 months ago • Options •
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Nice work. Check out my work too https://www.kaggle.com/umerkk12/titanic-dataset-eda-0-78